Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916948159234048 |
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| author | Xu, Huali Liu, Li Liu, Tianpeng Zhi, Shuaifeng Sun, Shuzhou Cheng, Ming-Ming |
| author_facet | Xu, Huali Liu, Li Liu, Tianpeng Zhi, Shuaifeng Sun, Shuzhou Cheng, Ming-Ming |
| contents | Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trained models (LMs) due to inaccessible source data and training strategies. Moreover, fine-tuning LMs for CDFSL demands substantial computational resources, limiting practicality. This paper addresses the source-free CDFSL (SF-CDFSL) problem, tackling few-shot learning (FSL) in the target domain using only pre-trained models and a few target samples without source data or strategies. To overcome the challenge of inaccessible source data, this paper introduces Step-wise Distribution Alignment Guided Style Prompt Tuning (StepSPT), which implicitly narrows domain gaps through prediction distribution optimization. StepSPT proposes a style prompt to align target samples with the desired distribution and adopts a dual-phase optimization process. In the external process, a step-wise distribution alignment strategy factorizes prediction distribution optimization into a multi-step alignment problem to tune the style prompt. In the internal process, the classifier is updated using standard cross-entropy loss. Evaluations on five datasets demonstrate that StepSPT outperforms existing prompt tuning-based methods and SOTAs. Ablation studies further verify its effectiveness. Code will be made publicly available at https://github.com/xuhuali-mxj/StepSPT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10070 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning Xu, Huali Liu, Li Liu, Tianpeng Zhi, Shuaifeng Sun, Shuzhou Cheng, Ming-Ming Computer Vision and Pattern Recognition Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trained models (LMs) due to inaccessible source data and training strategies. Moreover, fine-tuning LMs for CDFSL demands substantial computational resources, limiting practicality. This paper addresses the source-free CDFSL (SF-CDFSL) problem, tackling few-shot learning (FSL) in the target domain using only pre-trained models and a few target samples without source data or strategies. To overcome the challenge of inaccessible source data, this paper introduces Step-wise Distribution Alignment Guided Style Prompt Tuning (StepSPT), which implicitly narrows domain gaps through prediction distribution optimization. StepSPT proposes a style prompt to align target samples with the desired distribution and adopts a dual-phase optimization process. In the external process, a step-wise distribution alignment strategy factorizes prediction distribution optimization into a multi-step alignment problem to tune the style prompt. In the internal process, the classifier is updated using standard cross-entropy loss. Evaluations on five datasets demonstrate that StepSPT outperforms existing prompt tuning-based methods and SOTAs. Ablation studies further verify its effectiveness. Code will be made publicly available at https://github.com/xuhuali-mxj/StepSPT. |
| title | Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.10070 |